A comparative study of cluster based outlier detection, distance based outlier detection and density based outlier detection techniques

As there is an increasing demand of data, outlier detection is coming across as a popular field of research. Outlier is stated as an observation which is dissimilar from the other observations present in the data set. It is advantageous in the fields like medical industry, crime detection, fraudulent transaction, public safety etc. Outlier can be learnt in different fields like big data, time series data, high dimension data, biological data, uncertain data and many more. Most of the time 10% of the whole sample data set is incorrect, not accessible or missing sometimes. This paper studies and compares the popular outlier detection algorithms namely, Cluster based outlier detection, Distance based outlier detection and Density based outlier detection. Comparative study of these outlier detection techniques is performed to find out most efficient outlier detection method for calculation of the outlier.